An Examination of Under-studied Aspects of Ride-sourcing Adoption and Use in Large Metropolitan Areas
Bibliographic record
Abstract
Since they first entered the market in 2009, ride-sourcing has experienced significant growth in both popularity and utilization. The growing prevalence of ride-sourcing resulted in significant effort being dedicated to understanding the use and impacts of these services, which have highlighted the potential benefits and negative externalities associated with ride-sourcing. Consequently, understanding ride-sourcing adoption and use is crucial to mitigate the negative externalities and capitalize on the benefits associated with these services. This dissertation aims to expand on the current understanding of ride-sourcing adoption and use by building on the findings of studies published in the decade following the introduction of ride-sourcing. In pursuit of this goal, this dissertation examined aspects of ride-sourcing use that have received relatively little attention in the literature and explored different approaches for incorporating ride-sourcing into mode choice models. To help accomplish these objectives, web-based surveys were designed and conducted to collect information on ride-sourcing adoption and use in Toronto, the Greater Toronto Area, and Metro Vancouver. Using this data, statistical models and advanced econometric models were estimated to gain insights into ride-sourcing adoption and use. Examples include the estimation of a two-stage multinomial logistic regression model to understand the determinants of ride-sourcing adoption and user profile membership and the estimation of two-stage ordered generalized extreme value models to understand the determinants of anticipated post-pandemic ride-sourcing use. Additionally, error component mixed logit models and a joint RP-SP model were estimated and applied to explore the influence of ride-sourcing on the demand for existing modes. Besides, household travel survey data was used to explore different approaches for incorporating ride-sourcing into mode choice models. The findings presented in this dissertation can help inform efforts to mitigate the negative externalities associated with ride-sourcing and offer insights into the potential benefits of these services. Specifically, the results can contribute to efforts to encourage shared ride-sourcing use as well as initiatives to use on-demand services to serve areas where fixed-route transit may not feasible. Additionally, the results underscore the potential for post-pandemic ride-sourcing use to differ from that of pre-pandemic use, which could have important transportation planning implications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".